Recently, physics informed neural networks (PINNs) have been explored extensively for solving various forward and inverse problems and facilitating querying applications in fluid mechanics applications. However, work on PINNs for unsteady flows past moving bodies, such as flapping wings is scarce. Earlier studies mostly relied on transferring to a body attached frame of reference which is restrictive towards handling multiple moving bodies or deforming structures. Hence, in the present work, an immersed boundary aware framework has been explored for developing surrogate models for unsteady flows past moving bodies. Specifically, simultaneous pressure recovery and velocity reconstruction from Immersed boundary method (IBM) simulation data has been investigated. While, efficacy of velocity reconstruction has been tested against the fine resolution IBM data, as a step further, the pressure recovered was compared with that of an arbitrary Lagrange Eulerian (ALE) based solver. Under this framework, two PINN variants, (i) a moving-boundary-enabled standard Navier-Stokes based PINN (MB-PINN), and, (ii) a moving-boundary-enabled IBM based PINN (MB-IBM-PINN) have been formulated. A fluid-solid partitioning of the physics losses in MB-IBM-PINN has been allowed, in order to investigate the effects of solid body points while training. This enables MB-IBM-PINN to match with the performance of MB-PINN under certain loss weighting conditions. MB-PINN is found to be superior to MB-IBM-PINN when {\it a priori} knowledge of the solid body position and velocity are available. To improve the data efficiency of MB-PINN, a physics based data sampling technique has also been investigated. It is observed that a suitable combination of physics constraint relaxation and physics based sampling can achieve a model performance comparable to the case of using all the data points, under a fixed training budget.

翻译：近年来，物理信息神经网络（PINNs）已被广泛用于求解流体力学中的各类正反问题及查询应用。然而，针对拍动翼型等运动物体绕流非定常流动的PINNs研究仍十分有限。早期研究多依赖将坐标系转换至物体附着参考系的方法，这限制了处理多运动体或变形结构的能力。为此，本文探索了一种基于浸没边界感知的框架，用于构建运动物体绕流非定常流动的代理模型。具体而言，研究了从浸没边界法（IBM）仿真数据中同步重构压力与速度的方法。速度重构的有效性通过高分辨率IBM数据验证，进一步将压力恢复结果与基于任意拉格朗日-欧拉（ALE）求解器的结果进行对比。在该框架下，提出了两种PINNs变体：（i）支持移动边界的标准Navier-Stokes物理信息神经网络（MB-PINN），（ii）支持移动边界的基于IBM的物理信息神经网络（MB-IBM-PINN）。为探究训练过程中固体点的影响，在MB-IBM-PINN中引入了物理损失的流-固分区策略。这使得MB-IBM-PINN在特定损失权重条件下能够达到与MB-PINN相当的性能。研究表明，当先验已知固体位置与速度时，MB-PINN优于MB-IBM-PINN。为提升MB-PINN的数据效率，还探索了一种基于物理的数据采样技术。结果表明，在固定训练预算下，通过合理组合物理约束松弛与物理引导采样，可获得与使用全部数据点相当的模型性能。